PNNL evaluates bacterial, fungi, and algae strains as part of a four-step process to streamline and standardize biomanufacturing processing.

PNNL provides tools and expertise to bring faster, cost-effective catalysts to industry that can be produced at a large scale.

PNNL is contributing analytics expertise to a first-of-its-kind electric vehicle carsharing program in rural Hood River, Oregon.

PNNL and their collaborators are investigating combinations of fuels to boost vehicle efficiency, performance, and affordability.

The COMPASS project aims to increase fundamental understanding, models, and the ability to predict change in coastal systems.

PNNL is leading an effort to reveal the potential of algae for biofuels to replace conventional fuels for transportation.

The Data-Model Convergence (DMC) Initiative is a multidisciplinary effort to create the next generation of scientific computing capability through a software and hardware co-design methodology.

A multi-institution research team led by PNNL is addressing curb usage management challenges in large urban areas by developing a city-scale dynamic curb use simulation tool and an open-source curb management platform. 

PNNL is leading a consortium that provides funding opportunities to the automotive industry for accelerating new lightweight technologies in on-highway vehicles.

PNNL is a testbed for the latest research and technologies in marine carbon dioxide removal (mCDR)—leveraging the ocean’s strength as a natural carbon sink to address pressing climate concerns.

PNNL's Ocean Dynamics Modeling group studies coastal processes such as marine-hydrokinetic energy, coastal circulations, storm surge and extreme waves, tsunamis, sediment transport and nutrient-macroalgal dynamics.

PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.

The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.

The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.

The SEAHORCE project aims to improve the ability of the Energy Exascale Earth System Model to model oceans at higher fidelity than currently possible.

National laboratories, industry and academia are collaborating to provide electric vehicle manufacturers with batteries that are more reliable, high-performing, safe, and less expensive.

PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.